from dataclasses import dataclass
from typing import Any, TypeVar
import gymnasium as gym
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import to_torch
from tianshou.data.types import RolloutBatchProtocol
from tianshou.policy import QRDQNPolicy
from tianshou.policy.base import TLearningRateScheduler
from tianshou.policy.modelfree.qrdqn import QRDQNTrainingStats
[docs]
@dataclass(kw_only=True)
class DiscreteCQLTrainingStats(QRDQNTrainingStats):
cql_loss: float
qr_loss: float
TDiscreteCQLTrainingStats = TypeVar("TDiscreteCQLTrainingStats", bound=DiscreteCQLTrainingStats)
[docs]
class DiscreteCQLPolicy(QRDQNPolicy[TDiscreteCQLTrainingStats]):
"""Implementation of discrete Conservative Q-Learning algorithm. arXiv:2006.04779.
:param model: a model following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
:param optim: a torch.optim for optimizing the model.
:param action_space: Env's action space.
:param min_q_weight: the weight for the cql loss.
:param discount_factor: in [0, 1].
:param num_quantiles: the number of quantile midpoints in the inverse
cumulative distribution function of the value.
:param estimation_step: the number of steps to look ahead.
:param target_update_freq: the target network update frequency (0 if
you do not use the target network).
:param reward_normalization: normalize the **returns** to Normal(0, 1).
TODO: rename to return_normalization?
:param is_double: use double dqn.
:param clip_loss_grad: clip the gradient of the loss in accordance
with nature14236; this amounts to using the Huber loss instead of
the MSE loss.
:param observation_space: Env's observation space.
:param lr_scheduler: if not None, will be called in `policy.update()`.
.. seealso::
Please refer to :class:`~tianshou.policy.QRDQNPolicy` for more detailed
explanation.
"""
def __init__(
self,
*,
model: torch.nn.Module,
optim: torch.optim.Optimizer,
action_space: gym.spaces.Discrete,
min_q_weight: float = 10.0,
discount_factor: float = 0.99,
num_quantiles: int = 200,
estimation_step: int = 1,
target_update_freq: int = 0,
reward_normalization: bool = False,
is_double: bool = True,
clip_loss_grad: bool = False,
observation_space: gym.Space | None = None,
lr_scheduler: TLearningRateScheduler | None = None,
) -> None:
super().__init__(
model=model,
optim=optim,
action_space=action_space,
discount_factor=discount_factor,
num_quantiles=num_quantiles,
estimation_step=estimation_step,
target_update_freq=target_update_freq,
reward_normalization=reward_normalization,
is_double=is_double,
clip_loss_grad=clip_loss_grad,
observation_space=observation_space,
lr_scheduler=lr_scheduler,
)
self.min_q_weight = min_q_weight
[docs]
def learn(
self,
batch: RolloutBatchProtocol,
*args: Any,
**kwargs: Any,
) -> TDiscreteCQLTrainingStats:
if self._target and self._iter % self.freq == 0:
self.sync_weight()
self.optim.zero_grad()
weight = batch.pop("weight", 1.0)
all_dist = self(batch).logits
act = to_torch(batch.act, dtype=torch.long, device=all_dist.device)
curr_dist = all_dist[np.arange(len(act)), act, :].unsqueeze(2)
target_dist = batch.returns.unsqueeze(1)
# calculate each element's difference between curr_dist and target_dist
dist_diff = F.smooth_l1_loss(target_dist, curr_dist, reduction="none")
huber_loss = (
(dist_diff * (self.tau_hat - (target_dist - curr_dist).detach().le(0.0).float()).abs())
.sum(-1)
.mean(1)
)
qr_loss = (huber_loss * weight).mean()
# ref: https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/
# blob/master/fqf_iqn_qrdqn/agent/qrdqn_agent.py L130
batch.weight = dist_diff.detach().abs().sum(-1).mean(1) # prio-buffer
# add CQL loss
q = self.compute_q_value(all_dist, None)
dataset_expec = q.gather(1, act.unsqueeze(1)).mean()
negative_sampling = q.logsumexp(1).mean()
min_q_loss = negative_sampling - dataset_expec
loss = qr_loss + min_q_loss * self.min_q_weight
loss.backward()
self.optim.step()
self._iter += 1
return DiscreteCQLTrainingStats( # type: ignore[return-value]
loss=loss.item(),
qr_loss=qr_loss.item(),
cql_loss=min_q_loss.item(),
)
